Tag Archives: innovation

Architectural Innovation, Organizational Restructuring, and the Role of AI: Evidence from the Rise of Antibody-Drug Conjugates

Kwon, Angela Eunyoung, Jaecheok Park, Gene Moo Lee. “Architectural Innovation, Organizational Restructuring, and the Role of AI: Evidence from the Rise of Antibody-Drug Conjugates,” Work-in-progress.

  • Presentations: KrAIS (2026)

Architectural innovations reconfigure the linkages among existing components and thereby pose distinct challenges to established organizational structures. This study examines the rise of antibody-drug conjugates (ADCs) in oncology as a case of architectural innovation and investigates how this technological shift shapes organizational restructuring among pharmaceutical firms. Drawing on the theory of architectural innovation (Henderson & Clark,1990), we argue that ADCs disrupt established organizational routines and patterns of knowledge coordination, prompting firms engaged in oncology research to redesign internal roles, interfaces, and coordination mechanisms. We further theorize two moderators of firms’ adaptive effectiveness. First, AI capability functions as an architectural competence by facilitating cross-domain information processing and enabling knowledge recombination. Second, modality breadth provides absorptive capacity grounded in diverse drug development experience. This research-in-progress aims to contribute to the literature on architectural innovation and organizational restructuring by showing how firms adapt their internal structures to technological change, while also highlighting the emerging role of AI in firm-level knowledge coordination.

How Does AI Change Drug Development? Evidence from Clinical Trial Phases and Drug Types

Kwon, Angela Eunyoung, Jaecheok Park, Gene Moo Lee. “How Does AI Change Drug Development? Evidence from Clinical Trial Phases and Drug Types,” Working Paper.

  • Presentations: KrAIS (2025), CIST (2025), INFORMS (2025), UBC (2025), WISE (2025)

We examine how pharmaceutical firms’ AI capabilities influence drug development outcomes, focusing on clinical trials. Clinical trials progress through three phases that differ in regulatory scrutiny and evidentiary requirements. We measure firm-level AI capabilities using job postings and clinical trial outcomes using the number of trials initiated across phases. We find no significant overall effect of AI capabilities on clinical trial activity. However, this average relationship masks meaningful heterogeneity. AI capabilities are associated with increases in incremental innovation (refinement trials) but not radical innovation (new trials). These effects are stronger for biologics, where market incentives are high, than for small-molecule drugs, where learning hurdles are relatively low. AI capabilities also matter more in early-phase trials, where regulatory barriers are lower, and have no detectable influence in Phase III. This study contributes to the healthcare IS literature by identifying the nuanced and context-dependent business value of AI in drug development. It also offers practical guidance for pharmaceutical firms and policymakers on where AI investments are most likely to enhance R&D productivity.

Balance by Machine Redirection? The Role of Machine Learning Investments in Organizations’ Innovation Search and Long-Term Survival

Lee, Myunghwan, Timo Sturm, Gene Moo Lee, “Balance by Machine Redirection? The Role of Machine Learning Investments in Organizations’ Innovation Search and Long-Term Survival”, 2nd round R&R at MIS Quarterly.

  • Presentations: JUSWIS 2024, KrAIS Summer 2024
  • Best Short Paper Award at KrAIS Summer Workshop 2024.
  • RAs: Jeffrey Sun, Donghyun Nam

Organizations’ long-term survival depends on their ability to balance innovation search between explorative and exploitative innovation outputs. Machine learning (ML) investments can reshape this balance by enabling organizations to develop capabilities that generate innovation opportunities from data beyond those readily accessible through human-led innovation. Yet, it remains theoretically unclear whether ML helps organizations move toward greater balance or reinforces their innovation imbalances. We theorize that ML investments operate as a context-dependent directing mechanism: how they affect innovation search depends on organizations’ prior innovation tendencies and the types of ML capabilities they develop. We test this theory using a novel organization-year-level measure of ML investments in a longitudinal sample of 2,916 organizations. Combining panel regressions, long-difference analyses, survival models, and interviews with ML practitioners, we find that ML investments are associated with greater innovation balance by strengthening organizations’ underrepresented innovation tendencies: they redirect exploitation-oriented organizations toward explorative innovations and exploration-oriented ones toward exploitative innovations. The shift toward explorative innovations is strongest for organizations with unsupervised capabilities, and our survival and mediation analyses provide suggestive evidence that greater innovation balance may be linked to lower organizational failure risk. Our study extends research on IT-enabled innovation by showing that ML can become an additional source of innovation whose direction depends on organizations’ prior innovation search. In doing so, it offers a context-dependent view of ML-enabled innovation and explains how ML investments can help organizations counteract path-dependent innovation imbalances.

Myunghwan Lee’s PhD Proposal: Three Essays on AI Strategies and Innovation

Myunghwan Lee (2023) “Three Essays on AI Strategies and Innovation”, Ph.D. Dissertation Proposal, University of British Columbia. https://sites.google.com/view/myunghwanlee/home

Supervisor: Gene Moo Lee

Artificial Intelligence (AI) technologies, along with the explosive growth of digitized data, are transforming many industries and our society. While both academia and industry consider AI closely intertwined with innovation, we still have limited knowledge of the business and economic values of AI on innovation. This three-essay dissertation seeks to address this gap (i) by proposing a novel firm-level measure to identify strategically innovative firms; (ii) by examining how firm-level AI capabilities affect knowledge innovation; and (iii) by investigating the impact of robotics, embodied AI with a physical presence, on operational innovation.

In the first essay, we propose a novel firm-level measure, Strategic Competitive Positioning (SCP), to identify distinctive strategic positioning (i.e., first-movers, second-movers) and competition relationships. Drawing on network theory, we develop a structural hole-based, dynamic, and firm-specific SCP measure. Notably, this SCP measure is constructed using unsupervised machine-learning and network analytics approaches with minimal human intervention. Using a large dataset of 10-K annual reports from 13,476 public firms in the U.S., we demonstrate the value of the proposed measure by examining the impact of SCP on subsequent IPO performance.

In the second essay, we study the impact of firm-level AI capabilities on exploratory innovation to determine how AI’s value-creation process can facilitate knowledge innovation. Drawing on March and Simon (1958), we theorize how AI capabilities can help firms overcome bounded rationality and pursue exploratory innovation. We compiled a unique dataset consisting of 54,649 AI conference publications, 3 million patent filings, and 1.9 million inter-firm transactions to test the hypotheses. The findings show that a firm’s AI capabilities have a positive impact on exploratory innovation, and interestingly that conventional exploratory innovation-seeking approaches (e.g., traditional data management capabilities and inter-firm technology collaborations) negatively moderate the positive impact of AI capabilities on exploratory innovation.

The impact of AI technologies can be beyond knowledge innovation. Embodied AI technologies, specifically robotics, are driving operational innovation in manufacturing and service industries. While industrial robots designed for pre-defined tasks in controlled environments are extensively studied, little is known about the impact of AI-based service robots designed for customer-facing dynamic environments. In the third essay, we seek to examine how service robots can affect operational efficiency and service quality using the case of the hospitality industry. The preliminary results from a difference-in-differences model using a dataset of 4,610 restaurants in Singapore demonstrate that service robot adoption increases customer satisfaction, specifically through perceived service quality. To validate the initial result and further explore underlying mechanisms, we plan to collect additional datasets from different geographic areas and industries.

 

Disrupt with AI: The Impact of Deep Learning Capabilities on Exploratory Innovation

Lee, Myunghwan, Victor Cui, Gene Moo Lee. “Disrupt with AI: The Impact of Deep Learning Capabilities on Exploratory Innovation”, AOM 2023

Given the importance of exploratory innovation in fostering firms’ sustainable competitive advantages, firms often depend on technological assets or inter-firm relationships to pursue exploration. Regarded as a general-purpose technology, deep learning (DL)-based artificial intelligence (AI) can be an exploratory innovation-seeking instrument for firms in searching unexplored resources and thereby broadening their boundary. Drawing on the theories of organizational learning and path dependence, we hypothesize the impact of a firm’s DL capabilities on exploratory innovation and how DL capabilities interact with conventional pathbreaking activities such as technical assets and inter-firm relationships. Our empirical investigations, based on a novel DL capabilities measure constructed from comprehensive datasets on AI conferences and patents, show that DL capabilities have positive impacts on exploratory innovation. The results also show that extant technological assets (i.e., structured data management capabilities) and inter-firm relationships remedy the constraints on a firm’s innovation-seeking behaviors and that these path-breaking activities negatively moderate the positive impact of DL capabilities on exploratory innovation. To our knowledge, this is the first large-scale empirical study to investigate how DL affects exploratory innovation, contributing to the emerging literature on AI and innovation.

Ideas are Easy but Execution is Everything: Measuring the Impact of Stated AI Strategies and Capability on Firm Innovation Performance

Lee, Myunghwan, Gene Moo Lee (2022) “Ideas are Easy but Execution is Everything: Measuring the Impact of Stated AI Strategies and Capability on Firm Innovation Performance”, Work-in-Progress.

Contrary to the promise that AI will transform various industries, there are conflicting views on the impact of AI on firm performance. We argue that existing AI capability measures have two major limitations, limiting our understanding of the impact of AI in business. First, existing measures on AI capability do not distinguish between stated strategies and actual AI implementations. To distinguish stated AI strategy and actual AI capability, we collect various AI-related data sources, including AI conferences (e.g., NeurIPS, ICML, ICLR), patent filings (USPTO), inter-firm transactions related to AI adoption (FactSet), and AI strategies stated in 10-K annual reports. Second, while prior studies identified successful AI implementation factors (e.g., data integrity and intelligence augmentation) in a general context, little is known about the relationship between AI capabilities and in-depth innovation performance. We draw on the neo-institutional theory to articulate the firm-level AI strategies and construct a fine-grained AI capability measure that captures the unique characteristics of AI-strategy. Using our newly proposed AI capability measure and a novel dataset, we will study the impact of AI on firm innovation, contributing to the nascent literature on managing AI.